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5

Context Scaling

Your codebase is too big to paste in. Here's how to handle that.

📡
Context manager → pipeline architect

🎯 WHAT CHANGES BY THE END

You'll have a RAG pipeline on a real dataset and at least one structured-output workflow. Both will be in code you can modify, not a vendor platform you can't inspect.

🙋 THIS IS YOU IF

  • ✓You're hitting context limits on large codebases or document-heavy tasks
  • ✓Your pipeline outputs need to feed a database or another system, and free text doesn't work
  • ✓You've tried stuffing entire files into context and watched output quality degrade
  • ✓You're building an agent that needs to search across more than fits in one window

💡 WHAT WE'LL UNTANGLE

  • You're loading entire files or repos into context when only a section is relevant
  • Model outputs are unpredictable formats. You're parsing strings when you should have schemas
  • You don't know why retrieval is returning irrelevant results. No visibility into chunk quality
  • Every pipeline consumer is writing its own output parser
← PreviousLevel 4: Context EngineeringNext →Level 6: Compounding EngineeringLevel 7 gives the agent hands. It can write to your database, run your tests, and call your APIs.